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Record W2092097221 · doi:10.1121/1.4831490

Underwater acoustic measurements of an ultrasonic barrier for guidance of American Shad in front of hydroelectric installations

2013· article· en· W2092097221 on OpenAlexaff
François Lafleur

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsUnderwaterAcousticsEnvironmental scienceGeologyMarine engineeringContext (archaeology)SIGNAL (programming language)Spawn (biology)SonarComputer scienceOceanographyPhysicsEngineeringFishery

Abstract

fetched live from OpenAlex

This article presents the results of ultrasonic underwater acoustic measurements in a project guide shads during their downstream migration. Biological issues will be explained to allow the context, but the focus will be on the acoustic problems. In the spring, thousands of shad ascend the St. Lawrence River to spawn downstream of the Central Carillon. After spawning, adults return to sea heading toward the dam Rivière-des-Prairies. The configurations of the installed barriers at the Rivière-des-Prairies dam and at the ile Bizard site will be presented. A design of a signal amplifier was performed to optimize the barrier. A series of simulations and acoustic measurements have been conducted for the evaluation of the emission level of the barrier. The measurement strategy must take into account aspects such as high frequency signal (125 kHz) and geolocation measurement to allow achieving a mapping program of the barrier. The paper will describe: The issue biological; The deployment sites; The mechanism of hearing ultrasonic shad; Configuration of the barrier; The measurement system and the analysis of the results; Typical results obtained for mapping acoustic; Future directions in terms of signal measurements of the acoustic barrier.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.266
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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